Quantitative Finance COURSE NUMBER: 22:839:615 COURSE TITLE: Special Topics Oriented Programming 2 COURSE DESCRIPTION This course assumes a student has prior programming language experience with C++. It is designed for studying advance data structures using C++ programming language. Basic concepts such as time and space complexity analysis will be discussed. Data structures such as linked lists, stacks, queues, deques, backtracking with recursion and concepts of recursions will be covered. Different kinds of tree structures will be covered. The binary tree, binary search tree, heap, priority queues, Huffman tree will be discussed. Associate containers such as Maps, Sets, Hash tables, Hash maps will be covered. Different kinds of sorting methods and their time complexity analysis will be compared. SelfBalancing Search Tree structures such as AVL tree, Red-Black tree, and B-Tree among others will be discussed. Issues and concepts related to graphs will be covered. Some other interesting algorithms, algorithm development skills, and analysis will also be discussed. In addition to C++ programming language and its application to financial applications, Basic concept of databases such as SQL, database model, database design, and popular databases will be discussed. Python programming will be covered this semester. The basic data structures, and control structures of this programming language will be discussed. COURSE MATERIALS Suggest Reference Books: C++ How to Program, by P. J. Deitel and H. M. Deitel, 9 th ed. Prentice Hall Data Structures and Algorithm Analysis in C++, 4 th ed., by Mark Allen Weiss, Pearson Objects, Abstraction, Data Structures and Design Using C++, by Elliot B. Koffman, and Paul A. T. Wolfgang, Wiley Introduction to C++ for Financial Engineers: An Object-Oriented Approach, by D. Duffy, Wiley Murach s SQL Server 2012 for Developers, by Bryan Syverson, and Joel Murach, Murach Practice of Computing Using Python, 2/E, William F. Punch and Richard Enbody, ISBN-10: 013280557X ISBN-13: 9780132805575, 2013 Addison-Wesley Introduction to Programming using Python, Daniel Liang, ISBN 978-0-13-274718-9, Pearson 1
CLASS ORGANIZATION & ADMINISTRATION Course outline Review of C++ basics Review of software design and analysis Efficiencies and Correctness of programs Sequential containers reviews and enhancements Stacks Stack ADT Implementations of stacks Applications such as Postfix expression evaluations Converting among Prefix, Postfix, and Infix expressions Queues Queue ADT Implementations of queues Simulations using queue structures Deque and its implementations Backtracking using recursive constructs. Introduction of tree structures Tree traversals Implementation of tree structures Binary search tree and its operations Heap basic Heap implementation Priority queue Using heap as basis of priority queue Huffman coding tree Introduction of associate containers 2
Set and Multiset Map and Multimap Introduction of hash table Applications using hash tables Traversing of hash table and collision reductions Implementation of hash tables Selection Sort Bubble sort Insertion sort Shell sort Merge sort Heapsort Quick sort Time and space comparisons of different sorting algorithms Balancing and rotating on nodes of a tree The concepts of self-balancing search tree AVL tree and its implementation Red-Black trees and performance analysis 2-3 tree 2-3-4 and B-tree Directed and undirected graphs Adjacency list and Adjacency Matrix Breath first search of graph Depth first search of graph Shortest path Minimum spanning tree Introduction of structured query language 3
Microsoft SQL server basic Create databases and tables of relational databases Manipulation and retrieve data on relational database tables Introduction of Normal forms Brief introduction of open source database system: MySql Analysis huge datasets using non- Brief introduction of web site design and development traditional analysis approaches Concepts of supervised machine learning and unsupervised machine learning Apply artificial neural networks to predict and forecast financial and economic data Introduction of Python programming languages Decision tree classifier use in financial applications FINAL GRADE ASSIGMENT Homework and assignments 25% Exams 20% Midterm Project 10% Simulation Final Exam 25% Final Project 20% Prediction of trend of stocks using artificial neural network or other machine learning algorithms You may propose different project for any financial application subject to the approval of instructor Note Form a study group of 2 to 4 people and give a name for your group and designate a leader for your team; submit the information to your instructor; team leader hold responsibility to submit all assigned team homework, team assignment, or team project. The submission shall include all team members names Each individual student is responsible to submit his/her own independent homework, independent assignment, and independent project. Check e-mail for class cancellation in case of snow emergency, In case we have to meet online, a link will send to you so you can login to the virtual classroom. 4
Grades A >=92 A- 90-92 B+ 86-89 B 83-85 B- 80-82 C+ 76-79 C 73-75 C- 70-72 D+ 66-69 D 60-65 F <60 5